Cloud cover assessment for operational crop monitoring systems in tropical areas.

Abstract: The potential of optical remote sensing data to identify, map and monitor croplands is well recognized. However, clouds strongly limit the usefulness of optical imagery for these applications. This paper aims at assessing cloud cover conditions over four states in the tropical and sub-tropical Center-South region of Brazil to guide the development of an appropriate agricultural monitoring system based on Landsat-like imagery. Cloudiness was assessed during overlapping four months periods to match the typical length of crop cycles in the study area. The percentage of clear sky occurrence was computed from the 1 km resolution MODIS Cloud Mask product (MOD35) considering 14 years of data between July 2000 and June 2014. Results showed high seasonality of cloud occurrence within the crop year with strong variations across the study area. The maximum seasonality was observed for the two states in the northern part of the study area (i.e., the ones closer to the Equator line), which also presented the lowest averaged values (15%) of clear sky occurrence during the main (summer) cropping period (November to February). In these locations, optical data faces severe constraints for mapping summer crops. On the other hand, relatively favorable conditions were found in the southern part of the study region. In the South, clear sky values of around 45% were found and no signi?cant clear sky seasonality was observed. Results underpin the challenges to implement an operational crop monitoring system based solely on optical remote sensing imagery in tropical and sub-tropical regions, in particular if short-cycle crops have to be monitored during the cloudy summer months. To cope with cloudiness issues, we recommend the use of new systems with higher repetition rates such as Sentinel-2. For local studies, Unmanned Aircraft Vehicles(UAVs) might be used to augment the observing capability. Multi-sensor approaches combining optical and microwave data can be another option. In cases where wall-to-wall maps are not mandatory, statistical sampling approaches might also be a suitable alternative for obtaining useful crop area information

Flooded Area Mapping and Its Relationship to the Land Use, Soil Type, and Rainfall in North Konawe Regency

The flood incident in North Konawe Regency, Southeast Sulawesi that occurred on June 2nd, 2019 was the largest flood disaster in that area since the last 42 years, so it is interesting to study. As part of disaster risk management, it is necessary to do flood mapping to determine the distribution of flooded areas and identify areas that have potential for flooding. Mapping of flood inundation areas was carried out using Sentinel-1 data. Land use, rainfall and soil types are used as an analysis of their were relationship to the distribution of flood. The distribution of flood based on the identification of the presence of inundation covered 3 sub-districts, namely Oheo District, Asera District and Andowia District. Correlation of flood distribution to the land use, rainfall and soil type was identified using Pearson correlation value (r). The correlation between flood distribution and land use was -0.59 that indicates the correlation is moderate. Moreover, the correlation of flood distribution to the rainfall was 0 which means the correlation was very weak, and lastly, the correlation value of the flood distribution with soil type was 0.88 or the correlation was very strong

Lem benchmark database for tropical agricultural remote sensing application.

Abstract: The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic?s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data

Emerging vistas of Remote Sensing Tools in Pollination Studies

With the growth of information and technology across the globe, remote sensing applications find a place in the ecological studies of pollinators. The utilization of remote sensing tools in understanding the ecosystem services rendered by the bee pollinators is reviewed here. We discussed how radar and radio telemetry techniques helps to track individual bees, their foraging behaviour and density in relation to altered phenology of flowering crops in a landscape. Role of satellite imagery tools in studying characterizing a landscape affected by anthropogenic factors was discussed.  Monitoring invasive bee species that cause a threat to native bee fauna was explored. We explained the utilization of unmanned aerial vehicles to map the floral resource that influence the density and incidence of pollinators. Remote sensing tools used to measure sequence of pollination events was discussed

Análise da cobertura de nuvens no nordeste do Brasil e seus impactos no sensoriamento remoto agrícola operacional.

O presente trabalho tem como objetivo caracterizar a cobertura de nuvens para o Nordeste do Brasil nos doze meses do ano, bem como discutir os impactos para o monitoramento agrícola das principais áreas produtoras da região

Understanding the dynamic of tropical agriculture for remote sensing applications: a case study of Southeastern Brazil.

Abstract: The agricultural activity can greatly benefit from remote sensing technology (RS). Optical passive RS has been vastly explored for agricultural mapping and monitoring, in despite of cloud cover issue. This is observed even in the tropics, where frequency of clouds is very high. However, more studies are needed to better understand the high dynamism of tropical agriculture and its impact on the use of passive RS. In tropical countries, such as in Brazil, the use of current agricultural technologies, associated with favourable climate, allow the planting period to be wide and to have plants of varying phenological cycles. In this context, the main objective of the current study is to better understand the dynamics of a selected area in Southeast of São Paulo state, and its impact on the use of orbital passive RS. For that purpose, data (from field and satellite) from 55 agricultural fields, including annual, semi-perennial and perennial crops and silviculture, were acquired between July 2014 and December 2016. Field campaigns were conducted in a monthly base to gather information about the condition of the crops along their development (data available in a website). Field data corresponding to the 2014-2015 crop year were associated with a time series of Landsat-8/OLI RGB false-colour compositions images and MODIS/Terra NDVI profiles. The type of information that can be extracted (such as specie identification, crop management practices adopted, date of harvest, type o production system used etc) by combining passive remote sensing data with field data is discussed in the paper

LEM BENCHMARK DATABASE FOR TROPICAL AGRICULTURAL REMOTE SENSING APPLICATION

The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic’s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data

Banana mapping in heterogenous smallholder farming systems using high-resolution remote sensing imagery and machine learning models with implications for banana bunchy top disease surveillance

Open Access Journal; Published online: 18 Oct 2022Banana (and plantain, Musa spp.), in sub-Saharan Africa (SSA), is predominantly grown as a mixed crop by smallholder farmers in backyards and small farmlands, typically ranging from 0.2 ha to 3 ha. The crop is affected by several pests and diseases, including the invasive banana bunchy top virus (BBTV, genus Babuvirus), which is emerging as a major threat to banana production in SSA. The BBTV outbreak in West Africa was first recorded in the Benin Republic in 2010 and has spread to the adjoining territories of Nigeria and Togo. Regular surveillance, conducted as part of the containment efforts, requires the identification of banana fields for disease assessment. However, small and fragmented production spread across large areas poses complications for identifying all banana farms using conventional field survey methods, which is also time-consuming and expensive. In this study, we developed a remote sensing approach and machine learning (ML) models that can be used to identify banana fields for targeted BBTV surveillance. We used medium-resolution synthetic aperture radar (SAR), Sentinel 2A satellite imagery, and high-resolution RGB and multispectral aerial imagery from an unmanned aerial vehicle (UAV) to develop an operational banana mapping framework by combining the UAV, SAR, and Sentinel 2A data with the Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms. The ML algorithms performed comparatively well in classifying the land cover, with a mean overall accuracy (OA) of about 93% and a Kappa coefficient (KC) of 0.89 for the UAV data. The model using fused SAR and Sentinel 2A data gave an OA of 90% and KC of 0.86. The user accuracy (UA) and producer accuracy (PA) for the banana class were 83% and 78%, respectively. The BBTV surveillance teams used the banana mapping framework to identify banana fields in the BBTV-affected southwest Ogun state of Nigeria, which helped in detecting 17 sites with BBTV infection. These findings suggest that the prediction of banana and other crops in the heterogeneous smallholder farming systems is feasible, with the precision necessary to guide BBTV surveillance in large areas in SSA

Evaluating remotely piloted aircraft estimates of crop height and LAI against satellite and crop model outputs

Crop simulation models (CSM) have been a method for decision makers to study the effects of crop management activities for predicting, planning, and improving crop growth for the past several decades. While the applicability and robustness of CSMs had been rapidly evolving, the methods of gathering input and validation data for CSMs has remained predominantly the same. However, the application of remote sensing technologies including remotely piloted aircraft systems (RPAS) and satellites for agricultural purposes has demonstrated the potential for automated rapid and high detail CSM validation data. This study evaluated the accuracy of validation data acquired using RPAS and satellite technologies when compared to CSM outputs and observed crop measurements. Imagery of an agricultural field was acquired throughout a growing season with the use of a multi-sensor RPAS and existing satellite missions. Field work was performed alongside the RPAS imagery acquisitions to collect input data for crop modelling and accuracy assessments. Using the acquired imagery, the crop height and leaf area index (LAI) values of crops in the field were estimated for multiple dates. The LAI was estimated using 1) a regression-based method and 2) a function of the fractional vegetation cover and the leaf angle distribution method. A CSM was run alongside the remote sensing to simulate crop height and LAI values. When the estimated values were compared to observed measurements, showing the RPAS-derived crop height values were significantly more accurate (RMSE=193.6 cm, RMSE=161.3 cm) than the satellite-derived crop heights values (RMSE=223.4 m, RMSE=117.1 m respectively) yet less accurate than the CSM crop heights values. The RPAS-derived LAI value accuracies (RMSE=0.42, RMSE=0.66) and satellite-derived LAI value accuracies (RMSE=0.56, RMSE=0.56) were similar but the RPAS was found to, on average, estimate LAI more accurately than the CSM. Overall, the RPAS methods showed moderate accuracy across both crop height and LAI estimations and was found to perform better than the CSM in some situations. Future work may include additional imagery acquisitions throughout a growing season to further test the accuracies of RPAS-derived estimates as well as integrating estimates directly into CSMs for validation purposes